Aging of organ and abnormal tissue regeneration are recurrent problems in physiological and pathophysiological conditions. This is most crucial in case of high-turnover tissues, like bone marrow (BM). Using reciprocal transplantation experiments in mouse, we have shown that self-renewal potential of hematopoietic stem and progenitor cells (HSPCs) and BM cellularity are markedly influenced with the age of the recipient mice rather than donor mice. Moreover, accumulation of excessive reactive oxygen species (ROS) in BM stromal cells compared to HSPC compartment, in time-dependent manner, suggests that oxidative stress is involved in suppression of BM cellularity by affecting microenvironment in aged mice. Treatment of these mice with a polyphenolic antioxidant curcumin is found to partially quench ROS, thereby rescues stromal cells from oxidative stress-dependent cellular injury. This rejuvenation of stromal cells significantly improves hematopoietic reconstitution in 18-month-old mice compared to age control mice. In conclusion, this study implicates the role of ROS in perturbation of stromal cell function upon aging, which in turn affects BM's reconstitution ability in aged mice. Thus, a rejuvenation therapy using curcumin, before HSPC transplantation, is found to be an efficient strategy for successful marrow reconstitution in older mice.

During the reprogramming of mouse embryonic fibroblasts (MEFs) to induced pluripotent stem cells, the activation of pluripotency genes such as NANOG occurs after the mesenchymal to epithelial transition. Here we report that both adult stem cells (neural stem cells) and differentiated cells (astrocytes) of the neural lineage can activate NANOG in the absence of cadherin expression during reprogramming. Gene expression analysis revealed that only the NANOG+E-cadherin+ populations expressed stabilization markers, had upregulated several cell cycle genes; and were transgene independent. Inhibition of DOT1L activity enhanced both the numbers of NANOG+ and NANOG+E-cadherin+ colonies in neural stem cells. Expressing SOX2 in MEFs prior to reprogramming did not alter the ratio of NANOG colonies that express E-cadherin. Taken together these results provide a unique pathway for reprogramming taken by cells of the neural lineage.

Long range regulatory interactions among distal enhancers and target genes are important for tissue-specific gene expression. Genome-scale identification of these interactions in a cell line-specific manner, especially using the fewest possible datasets, is a significant challenge. We develop a novel computational approach, Regulatory Interaction Prediction for Promoters and Long-range Enhancers (RIPPLE), that integrates published Chromosome Conformation Capture (3C) data sets with a minimal set of regulatory genomic data sets to predict enhancer-promoter interactions in a cell line-specific manner. Our results suggest that CTCF, RAD21, a general transcription factor (TBP) and activating chromatin marks are important determinants of enhancer-promoter interactions. To predict interactions in a new cell line and to generate genome-wide interaction maps, we develop an ensemble version of RIPPLE and apply it to generate interactions in five human cell lines. Computational validation of these predictions using existing ChIA-PET and Hi-C data sets showed that RIPPLE accurately predicts interactions among enhancers and promoters. Enhancer-promoter interactions tend to be organized into subnetworks representing coordinately regulated sets of genes that are enriched for specific biological processes and cis-regulatory elements. Overall, our work provides a systematic approach to predict and interpret enhancer-promoter interactions in a genome-wide cell-type specific manner using a few experimentally tractable measurements.

Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.

An open chromatin largely devoid of heterochromatin is a hallmark of stem cells. It remains unknown whether an open chromatin is necessary for the differentiation potential of stem cells, and which molecules are needed to maintain open chromatin. Here we show that the chromatin remodelling factor Chd1 is required to maintain the open chromatin of pluripotent mouse embryonic stem cells. Chd1 is a euchromatin protein that associates with the promoters of active genes, and downregulation of Chd1 leads to accumulation of heterochromatin. Chd1-deficient embryonic stem cells are no longer pluripotent, because they are incapable of giving rise to primitive endoderm and have a high propensity for neural differentiation. Furthermore, Chd1 is required for efficient reprogramming of fibroblasts to the pluripotent stem cell state. Our results indicate that Chd1 is essential for open chromatin and pluripotency of embryonic stem cells, and for somatic cell reprogramming to the pluripotent state.

Elucidating the mechanism of reprogramming is confounded by heterogeneity due to the low efficiency and differential kinetics of obtaining induced pluripotent stem cells (iPSCs) from somatic cells. Therefore, we increased the efficiency with a combination of epigenomic modifiers and signaling molecules and profiled the transcriptomes of individual reprogramming cells. Contrary to the established temporal order, somatic gene inactivation and upregulation of cell cycle, epithelial, and early pluripotency genes can be triggered independently such that any combination of these events can occur in single cells. Sustained co-expression of Epcam, Nanog, and Sox2 with other genes is required to progress toward iPSCs. Ehf, Phlda2, and translation initiation factor Eif4a1 play functional roles in robust iPSC generation. Using regulatory network analysis, we identify a critical role for signaling inhibition by 2i in repressing somatic expression and synergy between the epigenomic modifiers ascorbic acid and a Dot1L inhibitor for pluripotency gene activation.

Seminal studies of vertebrate protein evolution speculated that gene regulatory changes can drive anatomical innovations. However, very little is known about gene regulatory network (GRN) evolution associated with phenotypic effect across ecologically diverse species. Here we use a novel approach for comparative GRN analysis in vertebrate species to study GRN evolution in representative species of the most striking examples of adaptive radiations, the East African cichlids. We previously demonstrated how the explosive phenotypic diversification of East African cichlids can be attributed to diverse molecular mechanisms, including accelerated regulatory sequence evolution and gene expression divergence.

Inhibiting the histone 3 lysine 79 (H3K79) methyltransferase, disruptor of telomeric silencing 1-like (DOT1L), increases the efficiency of reprogramming somatic cells to induced pluripotent stem cells (iPSCs). Here, we find that, despite the enrichment of H3K79 methylation on thousands of actively transcribed genes in somatic cells, DOT1L inhibition (DOT1Li) does not immediately cause the shutdown of the somatic transcriptional profile to enable transition to pluripotency. Contrary to the prevalent view, DOT1Li promotes iPSC generation beyond the mesenchymal to epithelial transition and even from already epithelial cell types. DOT1Li is most potent at the midpoint of reprogramming in part by repressing Nfix that persists at late stages of reprogramming. Importantly, regulation of single genes cannot substitute for DOT1Li, demonstrating that H3K79 methylation has pleiotropic effects in maintaining cell identity.

Cancer risk by environmental exposure is modulated by an individual's genetics and age at exposure. This age-specific period of susceptibility is referred to as the "Window of Susceptibility" (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.

The divergence of regulatory regions and gene regulatory network (GRN) rewiring is a key driver of cichlid phenotypic diversity. However, the contribution of miRNA-binding site turnover has yet to be linked to GRN evolution across cichlids. Here, we extend our previous studies by analyzing the selective constraints driving evolution of miRNA and transcription factor (TF)-binding sites of target genes, to infer instances of cichlid GRN rewiring associated with regulatory binding site turnover. Comparative analyses identified increased species-specific networks that are functionally associated to traits of cichlid phenotypic diversity. The evolutionary rewiring is associated with differential models of miRNA- and TF-binding site turnover, driven by a high proportion of fast-evolving polymorphic sites in adaptive trait genes compared with subsets of random genes. Positive selection acting upon discrete mutations in these regulatory regions is likely to be an important mechanism in rewiring GRNs in rapidly radiating cichlids. Regulatory variants of functionally associated miRNA- and TF-binding sites of visual opsin genes differentially segregate according to phylogeny and ecology of Lake Malawi species, identifying both rewired, for example, clade-specific and conserved network motifs of adaptive trait associated GRNs. Our approach revealed several novel candidate regulators, regulatory regions, and three-node motifs across cichlid genomes with previously reported associations to known adaptive evolutionary traits.

Dominantly inherited disorders are not typically considered to be therapeutic candidates for gene augmentation. Here, we utilized induced pluripotent stem cell-derived retinal pigment epithelium (iPSC-RPE) to test the potential of gene augmentation to treat Best disease, a dominant macular dystrophy caused by over 200 missense mutations in BEST1. Gene augmentation in iPSC-RPE fully restored BEST1 calcium-activated chloride channel activity and improved rhodopsin degradation in an iPSC-RPE model of recessive bestrophinopathy as well as in two models of dominant Best disease caused by different mutations in regions encoding ion-binding domains. A third dominant Best disease iPSC-RPE model did not respond to gene augmentation, but showed normalization of BEST1 channel activity following CRISPR-Cas9 editing of the mutant allele. We then subjected all three dominant Best disease iPSC-RPE models to gene editing, which produced premature stop codons specifically within the mutant BEST1 alleles. Single-cell profiling demonstrated no adverse perturbation of retinal pigment epithelium (RPE) transcriptional programs in any model, although off-target analysis detected a silent genomic alteration in one model. These results suggest that gene augmentation is a viable first-line approach for some individuals with dominant Best disease and that non-responders are candidates for alternate approaches such as gene editing. However, testing gene editing strategies for on-target efficiency and off-target events using personalized iPSC-RPE model systems is warranted. In summary, personalized iPSC-RPE models can be used to select among a growing list of gene therapy options to maximize safety and efficacy while minimizing time and cost. Similar scenarios likely exist for other genotypically diverse channelopathies, expanding the therapeutic landscape for affected individuals.

Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type-specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type-specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type-specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

Antibiotic treatment for pneumonia as measured by Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) is a key indicator for tracking progress in achieving Millennium Development Goal 4. Concerns about the validity of this indicator led us to perform an evaluation in urban and rural settings in Pakistan and Bangladesh.

Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer types that identify common and cancer-specific network components. We present a module- and network-based characterization of transcriptional patterns in six different cancers being studied in TCGA: breast, colon, rectal, kidney, ovarian, and endometrial. Our approach uses a recently developed regulatory network reconstruction algorithm, modular regulatory network learning with per gene information (MERLIN), within a stability selection framework to predict regulators for individual genes and gene modules. Our module-based analysis identifies a common theme of immune system processes in each cancer study, with modules statistically enriched for immune response processes as well as targets of key immune response regulators from the interferon regulatory factor (IRF) and signal transducer and activator of transcription (STAT) families. Comparison of the inferred regulatory networks from each cancer type identified a core regulatory network that included genes involved in chromatin remodeling, cell cycle, and immune response. Regulatory network hubs included genes with known roles in specific cancer types as well as genes with potentially novel roles in different cancer types. Overall, our integrated module and network analysis recapitulated known themes in cancer biology and additionally revealed novel regulatory hubs that suggest a complex interplay of immune response, cell cycle, and chromatin remodeling across multiple cancers.

Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) represents a profound change in cell fate. Here, we show that combining ascorbic acid (AA) and 2i (MAP kinase and GSK inhibitors) increases the efficiency of reprogramming from fibroblasts and synergistically enhances conversion of partially reprogrammed intermediates to the iPSC state. AA and 2i induce differential transcriptional responses, each leading to the activation of specific pluripotency loci. A unique cohort of pluripotency genes including Esrrb require both stimuli for activation. Temporally, AA-dependent histone demethylase effects are important early, whereas Tet enzyme effects are required throughout the conversion. 2i function could partially be replaced by depletion of components of the epidermal growth factor (EGF) and insulin growth factor pathways, indicating that they act as barriers to reprogramming. Accordingly, reduction in the levels of the EGF receptor gene contributes to the activation of Esrrb. These results provide insight into the rewiring of the pluripotency network at the late stage of reprogramming.

The heterochromatin protein 1 (HP1) family is involved in various functions with maintenance of chromatin structure. During murine somatic cell reprogramming, we find that early depletion of HP1γ reduces the generation of induced pluripotent stem cells, while late depletion enhances the process, with a concomitant change from a centromeric to nucleoplasmic localization and elongation-associated histone H3.3 enrichment. Depletion of heterochromatin anchoring protein SENP7 increased reprogramming efficiency to a similar extent as HP1γ, indicating the importance of HP1γ release from chromatin for pluripotency acquisition. HP1γ interacted with OCT4 and DPPA4 in HP1α and HP1β knockouts and in H3K9 methylation depleted H3K9M embryonic stem cell (ESC) lines. HP1α and HP1γ complexes in ESCs differed in association with histones, the histone chaperone CAF1 complex, and specific components of chromatin-modifying complexes such as DPY30, implying distinct functional contributions. Taken together, our results reveal the complex contribution of the HP1 proteins to pluripotency.

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

Autosomal dominant polycystic kidney disease (ADPKD) caused by mutations in PKD1 is significantly more severe than PKD2. Typically, ADPKD presents in adulthood but is rarely diagnosed in utero with enlarged, echogenic kidneys. Somatic mutations are thought crucial for cyst development, but gene dosage is also important since animal models with hypomorphic alleles develop cysts, but are viable as homozygotes. We screened for mutations in PKD1 and PKD2 in two consanguineous families and found PKD1 missense variants predicted to be pathogenic. In one family, two siblings homozygous for R3277C developed end stage renal disease at ages 75 and 62 years, while six heterozygotes had few cysts. In the other family, the father and two children with moderate to severe disease were homozygous for N3188S. In both families homozygous disease was associated with small cysts of relatively uniform size while marked cyst heterogeneity is typical of ADPKD. In another family, one patient diagnosed in childhood was found to be a compound heterozygote for the PKD1 variants R3105W and R2765C. All three families had evidence of developmental defects of the collecting system. Three additional ADPKD families with in utero onset had a truncating mutation in trans with either R3277C or R2765C. These cases suggest the presence of incompletely penetrant PKD1 alleles. The alleles alone may result in mild cystic disease; two such alleles cause typical to severe disease; and, in combination with an inactivating allele, are associated with early onset disease. Our study indicates that the dosage of functional PKD1 protein may be critical for cyst initiation.

S-adenosylmethionine (SAM) is the methyl-donor substrate for DNA and histone methyltransferases that regulate epigenetic states and subsequent gene expression. This metabolism-epigenome link sensitizes chromatin methylation to altered SAM abundance, yet the mechanisms that allow organisms to adapt and protect epigenetic information during life-experienced fluctuations in SAM availability are unknown. We identified a robust response to SAM depletion that is highlighted by preferential cytoplasmic and nuclear mono-methylation of H3 Lys 9 (H3K9) at the expense of broad losses in histone di- and tri-methylation. Under SAM-depleted conditions, H3K9 mono-methylation preserves heterochromatin stability and supports global epigenetic persistence upon metabolic recovery. This unique chromatin response was robust across the mouse lifespan and correlated with improved metabolic health, supporting a significant role for epigenetic adaptation to SAM depletion in vivo. Together, these studies provide evidence for an adaptive response that enables epigenetic persistence to metabolic stress.

The three-dimensional (3D) organization of the genome plays an important role in gene regulation bringing distal sequence elements in 3D proximity to genes hundreds of kilobases away. Hi-C is a powerful genome-wide technique to study 3D genome organization. Owing to experimental costs, high resolution Hi-C datasets are limited to a few cell lines. Computational prediction of Hi-C counts can offer a scalable and inexpensive approach to examine 3D genome organization across multiple cellular contexts. Here we present HiC-Reg, an approach to predict contact counts from one-dimensional regulatory signals. HiC-Reg predictions identify topologically associating domains and significant interactions that are enriched for CCCTC-binding factor (CTCF) bidirectional motifs and interactions identified from complementary sources. CTCF and chromatin marks, especially repressive and elongation marks, are most important for HiC-Reg's predictive performance. Taken together, HiC-Reg provides a powerful framework to generate high-resolution profiles of contact counts that can be used to study individual locus level interactions and higher-order organizational units of the genome.